Wintertime ocean conditions synchronize rockfish growth and seabird reproduction in the central California Current ecosystem
Bibliographic record
Abstract
Chronologies developed from annual growth-increment widths of splitnose rockfish ( Sebastes pinniger ) and yelloweye rockfish ( Sebastes ruberrimus ) otoliths were compared with time series of lay date and fledgling success for the common murre ( Uria aalge ) and Cassin’s auklet ( Ptychoramphus aleuticus ) in the north-central California Current. All time series were exactly dated and spanned 1972 through 1994. In a principal components analysis, the leading principal component (PC1bio) accounted for 64% of the variance in the data set. By entering the upwelling index, the Northern Oscillation index, sea surface temperatures, and the multivariate ENSO (El Niño Southern Oscillation) index into principal components analysis, a time series of environmental variability PC1env was developed for each month of the year. Over the interval 1972 through 1994, PC1bio most strongly correlated with PC1env for February and, to a lesser extent, January and March. Moreover, when each of the six biological time series was related to the 12 PC1env through stepwise multiple regression, February was always the most significant (p < 0.01). The same was true if upwelling index was substituted for PC1env. As upper-trophic predators, rockfish and seabirds independently corroborate that wintertime ocean conditions are critical for productivity in the California Current ecosystem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".